This forum is for questions related to the use of Apollo. We will answer some general choice modelling questions too, where appropriate, and time permitting. We cannot answer questions about how to estimate choice models with other software packages.
Before asking a question on the forum, users are kindly requested to follow these steps:
Check that the same issue has not already been addressed in the forum - there is a search tool.
Ensure that the correct syntax has been used. For any function, detailed instructions are available directly in Apollo, e.g. by using ?apollo_mnl for apollo_mnl
To update to the latest official version, just enter install.packages("apollo"). To update to a development version, download the appropriate binary file from http://www.ApolloChoiceModelling.com/code.html, and install the package from file
If the above steps do not resolve the issue, then users should follow these steps when posting a question:
provide full details on the issue, including the entire code and output, including any error messages
posts will not immediately appear on the forum, but will be checked by a moderator first. This may take a day or two at busy times. There is no need to submit the post multiple times.
Report bugs or highlight issues with Apollo functions. At a minimum, please include the part of the output where you believe the bug is manifested. Ideally, please share your model file.
forecast = apollo_prediction(model, apollo_probabilities, inputs)
Your model contains continuous random parameters. apollo_prediction will perform averaging across draws
for these. For predictions at the level of individual draws, please call the apollo_probabilities
function using model$estimate as the parameters, and with functionality="raw".
Running predictions from model using parameter estimates...
Predicted aggregated demand at model estimates
Error in colSums(M, na.rm = TRUE) : 'x' must be numeric
I'm running this on a Mac using version 0.2.2 and R 4.0.3.
thanks for sending me the data to test. This is indeed a bug, and is caused by the fact that the respondent IDs in your data are characters rather than numbers. R then turns the whole matrix of predictions into characters, and then can't take the average.
Until we fix this bug in v.0.2.4, the easy workaround for you is to turn your IDs into numeric ones
Just so you know, I've also noticed that posterior parameter means and standard deviations obtained using apollo_conditionals are returned as characters instead of numbers. It's not much of a problem, but the same numbers to characters issue could be behind that.